



from __future__ import annotations

from typing import Generic, TypeVar

a      :  List[Any]			     =			TypeVar('T')







class 				_a				(   Generic[T]					):

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   None:
						UpperCAmelCase_:		str							      =     data
						UpperCAmelCase_:		int							      =     self
						UpperCAmelCase_:		Any							      =     0







class 				_a				(   Generic[T]					):

			def __init__(self					)       ->   None:
						# map from node name to the node object
						UpperCAmelCase_:		dict[T, DisjointSetTreeNode[T]]							      =     {}

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   None:
						# create a new set with x as its member
						UpperCAmelCase_:		str							      =     DisjointSetTreeNode(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   DisjointSetTreeNode[T]:
						# find the set x belongs to (with path-compression)
						UpperCAmelCase_:		Union[str, Any]							      =     self.map[data]
						if elem_ref != elem_ref.parent:
									UpperCAmelCase_:		Union[str, Any]							      =     self.find_set(elem_ref.parent.data					)
						return elem_ref.parent

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   None:
						# helper function for union operation
						if nodea.rank > nodea.rank:
									UpperCAmelCase_:		Union[str, Any]							      =     nodea
						else:
									UpperCAmelCase_:		int							      =     nodea
									if nodea.rank == nodea.rank:
												nodea.rank += 1

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   None:
						# merge 2 disjoint sets
						self.link(self.find_set(SCREAMING_SNAKE_CASE_					),   self.find_set(SCREAMING_SNAKE_CASE_					)					)







class 				_a				(   Generic[T]					):

			def __init__(self					)       ->   None:
						# connections: map from the node to the neighbouring nodes (with weights)
						UpperCAmelCase_:		dict[T, dict[T, int]]							      =     {}

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   None:
						# add a node ONLY if its not present in the graph
						if node not in self.connections:
									UpperCAmelCase_:		Tuple							      =     {}

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   None:
						# add an edge with the given weight
						self.add_node(SCREAMING_SNAKE_CASE_					)
						self.add_node(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     weight
						UpperCAmelCase_:		List[Any]							      =     weight

			def      __snake_case       (self					)       ->   GraphUndirectedWeighted[T]:
						UpperCAmelCase_:		str							      =     []
						UpperCAmelCase_:		Optional[Any]							      =     set()
						for start in self.connections:
									for end in self.connections[start]:
												if (start, end) not in seen:
															seen.add((end, start)					)
															edges.append((start, end, self.connections[start][end])					)
						edges.sort(key=lambda SCREAMING_SNAKE_CASE_					: x[2]					)

						# creating the disjoint set
						UpperCAmelCase_:		str							      =     DisjointSetTree[T]()
						for node in self.connections:
									disjoint_set.make_set(SCREAMING_SNAKE_CASE_					)

						# MST generation
						UpperCAmelCase_:		Union[str, Any]							      =     0
						UpperCAmelCase_:		str							      =     0
						UpperCAmelCase_:		Tuple							      =     GraphUndirectedWeighted[T]()
						while num_edges < len(self.connections					) - 1:
									UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     edges[index]
									index += 1
									UpperCAmelCase_:		Dict							      =     disjoint_set.find_set(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Any							      =     disjoint_set.find_set(SCREAMING_SNAKE_CASE_					)
									if parent_u != parent_v:
												num_edges += 1
												graph.add_edge(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
												disjoint_set.union(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						return graph











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






